{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import keras\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from  pathlib import Path\n",
    "data_path = r\"D:\\project\\tianchi\\dont-overfit-ii\"\n",
    "train_file=r\"train.csv\"\n",
    "\n",
    "df=pd.read_csv(Path(data_path,train_file))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>...</th>\n",
       "      <th>290</th>\n",
       "      <th>291</th>\n",
       "      <th>292</th>\n",
       "      <th>293</th>\n",
       "      <th>294</th>\n",
       "      <th>295</th>\n",
       "      <th>296</th>\n",
       "      <th>297</th>\n",
       "      <th>298</th>\n",
       "      <th>299</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.098</td>\n",
       "      <td>2.165</td>\n",
       "      <td>0.681</td>\n",
       "      <td>-0.614</td>\n",
       "      <td>1.309</td>\n",
       "      <td>-0.455</td>\n",
       "      <td>-0.236</td>\n",
       "      <td>0.276</td>\n",
       "      <td>...</td>\n",
       "      <td>0.867</td>\n",
       "      <td>1.347</td>\n",
       "      <td>0.504</td>\n",
       "      <td>-0.649</td>\n",
       "      <td>0.672</td>\n",
       "      <td>-2.097</td>\n",
       "      <td>1.051</td>\n",
       "      <td>-0.414</td>\n",
       "      <td>1.038</td>\n",
       "      <td>-1.065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.081</td>\n",
       "      <td>-0.973</td>\n",
       "      <td>-0.383</td>\n",
       "      <td>0.326</td>\n",
       "      <td>-0.428</td>\n",
       "      <td>0.317</td>\n",
       "      <td>1.172</td>\n",
       "      <td>0.352</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.165</td>\n",
       "      <td>-1.695</td>\n",
       "      <td>-1.257</td>\n",
       "      <td>1.359</td>\n",
       "      <td>-0.808</td>\n",
       "      <td>-1.624</td>\n",
       "      <td>-0.458</td>\n",
       "      <td>-1.099</td>\n",
       "      <td>-0.936</td>\n",
       "      <td>0.973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.523</td>\n",
       "      <td>-0.089</td>\n",
       "      <td>-0.348</td>\n",
       "      <td>0.148</td>\n",
       "      <td>-0.022</td>\n",
       "      <td>0.404</td>\n",
       "      <td>-0.023</td>\n",
       "      <td>-0.172</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.263</td>\n",
       "      <td>-1.222</td>\n",
       "      <td>0.726</td>\n",
       "      <td>1.444</td>\n",
       "      <td>-1.165</td>\n",
       "      <td>-1.544</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.800</td>\n",
       "      <td>-1.211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.067</td>\n",
       "      <td>-0.021</td>\n",
       "      <td>0.392</td>\n",
       "      <td>-1.637</td>\n",
       "      <td>-0.446</td>\n",
       "      <td>-0.725</td>\n",
       "      <td>-1.035</td>\n",
       "      <td>0.834</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.404</td>\n",
       "      <td>0.640</td>\n",
       "      <td>-0.595</td>\n",
       "      <td>-0.966</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.467</td>\n",
       "      <td>-0.562</td>\n",
       "      <td>-0.254</td>\n",
       "      <td>-0.533</td>\n",
       "      <td>0.238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.347</td>\n",
       "      <td>-0.831</td>\n",
       "      <td>0.511</td>\n",
       "      <td>-0.021</td>\n",
       "      <td>1.225</td>\n",
       "      <td>1.594</td>\n",
       "      <td>0.585</td>\n",
       "      <td>1.509</td>\n",
       "      <td>...</td>\n",
       "      <td>0.898</td>\n",
       "      <td>0.134</td>\n",
       "      <td>2.415</td>\n",
       "      <td>-0.996</td>\n",
       "      <td>-1.006</td>\n",
       "      <td>1.378</td>\n",
       "      <td>1.246</td>\n",
       "      <td>1.478</td>\n",
       "      <td>0.428</td>\n",
       "      <td>0.253</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 302 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  target      0      1      2      3      4      5      6      7  ...    \\\n",
       "0   0     1.0 -0.098  2.165  0.681 -0.614  1.309 -0.455 -0.236  0.276  ...     \n",
       "1   1     0.0  1.081 -0.973 -0.383  0.326 -0.428  0.317  1.172  0.352  ...     \n",
       "2   2     1.0 -0.523 -0.089 -0.348  0.148 -0.022  0.404 -0.023 -0.172  ...     \n",
       "3   3     1.0  0.067 -0.021  0.392 -1.637 -0.446 -0.725 -1.035  0.834  ...     \n",
       "4   4     1.0  2.347 -0.831  0.511 -0.021  1.225  1.594  0.585  1.509  ...     \n",
       "\n",
       "     290    291    292    293    294    295    296    297    298    299  \n",
       "0  0.867  1.347  0.504 -0.649  0.672 -2.097  1.051 -0.414  1.038 -1.065  \n",
       "1 -0.165 -1.695 -1.257  1.359 -0.808 -1.624 -0.458 -1.099 -0.936  0.973  \n",
       "2  0.013  0.263 -1.222  0.726  1.444 -1.165 -1.544  0.004  0.800 -1.211  \n",
       "3 -0.404  0.640 -0.595 -0.966  0.900  0.467 -0.562 -0.254 -0.533  0.238  \n",
       "4  0.898  0.134  2.415 -0.996 -1.006  1.378  1.246  1.478  0.428  0.253  \n",
       "\n",
       "[5 rows x 302 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 2.,  1.,  2.,  2.,  3.,  3.,  4.,  4.,  6., 10.,  5.,  6.,  9.,\n",
       "        11.,  5., 15.,  7., 15., 16., 14., 15., 10.,  8.,  8., 10.,  8.,\n",
       "         7.,  4.,  6.,  6.,  7.,  4.,  3.,  5.,  1.,  2.,  2.,  2.,  1.,\n",
       "         1.]), array([-2.31900e+00, -2.19685e+00, -2.07470e+00, -1.95255e+00,\n",
       "        -1.83040e+00, -1.70825e+00, -1.58610e+00, -1.46395e+00,\n",
       "        -1.34180e+00, -1.21965e+00, -1.09750e+00, -9.75350e-01,\n",
       "        -8.53200e-01, -7.31050e-01, -6.08900e-01, -4.86750e-01,\n",
       "        -3.64600e-01, -2.42450e-01, -1.20300e-01,  1.85000e-03,\n",
       "         1.24000e-01,  2.46150e-01,  3.68300e-01,  4.90450e-01,\n",
       "         6.12600e-01,  7.34750e-01,  8.56900e-01,  9.79050e-01,\n",
       "         1.10120e+00,  1.22335e+00,  1.34550e+00,  1.46765e+00,\n",
       "         1.58980e+00,  1.71195e+00,  1.83410e+00,  1.95625e+00,\n",
       "         2.07840e+00,  2.20055e+00,  2.32270e+00,  2.44485e+00,\n",
       "         2.56700e+00]), <a list of 1 Patch objects>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df[\"0\"], 40, histtype='stepfilled', orientation='vertical', color='deeppink')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1bc38fb1390>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "view=df[:10]\n",
    "plt.scatter(x='id',y='1',data=view)\n",
    "plt.scatter(x='id',y='0',data=view)\n",
    "plt.scatter(x='id',y='target',data=view,color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
